Machine Learning Questions and Answers

Machine Learning is a hot technology that permits computers to study without delay from examples and revel in within the form of data. Traditional methods to programming rely on hardcoded guidelines, which set out a way to clear up a trouble, step-by-step. In evaluation, Machine learning structures are set a project, and given a massive quantity of data to apply as examples of the way this task may be finished or from which to stumble on styles. The system then learns how great to achieve the desired output. It may be concept of as narrow AI: machine learning supports shrewd systems, which are capable of analyze a particular function, given a selected set of data to examine from. Machine learning is a modern and fantastically sophisticated technological utility of a long set up belief look at the beyond to expect the destiny.

Machine learning is subset of Artificial Intelligence (AI). Machine learning is the area of Computer science that specializes in analyzing and interpreting styles and structures in data to allow enable knowledge of, reasoning, and decision making outdoor of human interaction.

Deep Learning (DL) is a subset of Machine Learning (ML). Usually, when people use the term deep learning, they are referring to deep artificial neural networks, and somewhat less frequently to deep reinforcement learning.

Machine learning has several very practical applications that drive the kind of real business results – such as time and money savings – that have the potential to dramatically impact the future of your organization.. Machine learning has made dramatic improvements in the past few years, but we are still very far from reaching human performance. Many times, the machine needs the assistance of human to complete its task. At Interactions, we have deployed Virtual Assistant solutions that seamlessly blend artificial with true human intelligence to deliver the highest level of accuracy and understanding. However, machine learning is best leveraged for specific types of applications that will benefit the most from this technology, such as fraud detection, predictive marketing, machine monitoring (for the Internet of Things), and inventory management. The most intuitive and prominent example is self-driving cars.

Reinforcement learning, the model has some input data and a reward depending on the output of the model. The model learns a policy that maximizes the reward. Reinforcement learning has been applied successfully to strategic games such as Go and even classic Atari video games.

Data Pre-processing is a technique that is used to convert the raw data into a clean data set. In other words, whenever the data is gathered from different sources it is collected in raw format which is not feasible for the analysis.

Your pre-processed data may contain attributes with mixtures of scales for various quantities such as dollars, kilograms and sales volume. For data with attributes of varying scales, we can rescale attributes to possess the same scale. We rescale attributes into the range 0 to 1 and call it normalization. We use the MinMaxScaler class from scikit-learn.

Standardization refers to shifting the distribution of each attribute to have a mean of zero and a standard deviation of one (unit variance).It is useful to standardize attributes for a model that relies on the distribution of attributes such as Gaussian processes.

Normalization refers to rescaling real valued numeric attributes into the range 0 and 1.It is useful to scale the input attributes for a model that relies on the magnitude of values, such as distance measures used in k-nearest neighbours and in the preparation of coefficients in regression.

Data augmentation is a technique for synthesizing new data by modifying existing data in such a way that the target is not changed, or it is changed in a known way. Computer vision is one of fields where data augmentation is very useful

Artificial Intelligence (AI) involves machines that execute tasks which are programmed and based on human intelligence, whereas ML is a subset application of AI where machines are made to learn information. They gradually perform tasks and can automatically build models from the learning’s

K-nearest algorithm is the supervised learning while the k-means algorithm is assigned under the unsupervised learning. While these two techniques look similar at the first glance, still there is a lot of difference between the two. Supervised learning needs data in the labelled form.

Find missing/corrupted data in a dataset and either drop those rows or columns, or decide to replace them with another value. In Pandas, there are two very useful methods: isnull() and dropna() that will help you find columns of data with missing or corrupted data and drop those values. If you want to fill the invalid values with a placeholder value (for example, 0), you could use the fillna() method.

The process of reducing variables in a ML classification scenario is called Dimensionality reduction. The process is segregated into sub-processes called feature extraction and feature selection. Dimensionality reduction is done to enhance visualisation of training data. It finds the appropriate set of variables known as principal variables.

PCA stands for Principal Component Analysis. It is a dimensionality-reduction technique which mathematically transforms a set of correlated variables into a smaller set of uncorrelated variables called principal components. Applications of PCA are Noise reduction, Preprocess, Compression.

The transformation stage in the data preparation process includes an important step known as Feature Engineering. Feature Engineering refers to selecting and extracting the right features from the data that are relevant to the task and model in consideration.

Batch Normalization is a technique to provide any layer in a Neural Network with inputs that are zero mean/unit variance – and this is basically what they like! But BatchNorm consists of one more step which makes this algorithm really powerful.

Parameters are attributes in training data that can be estimated during ML. Hyperparameters are attributes that cannot be determined beforehand in the training data. Example: Learning rate in neural networks.

Pruning is you remove branches that have weak predictive power in order to reduce the complexity of the model and in addition increase the predictive accuracy of a decision tree model. There are several flavours which includes, bottom-up and top-down pruning, with approaches such as reduced error pruning and cost complexity pruning.

ROC stands for Receiver operating characteristic .It is the pictorial representation of the contrast between true positive rates and the false positive rates calculated at multiple thresholds. It is used as the proxy to measure the trade-offs and sensitivity of the model. Based on the observation, it will trigger the false alarms.

The bias-variance trade-off is able to handle the learning errors effectively and manages noise too that happens due to underlying data, essentially, this trade-off will make the model more complex than usual but errors are reduced optimally

One can find missing data in a data-set and either drop those rows or columns, or decide to replace them with another value. In python library Pandas there are two useful functions which will be helpful, isnull() and dropna().

Type I error is committed when the null hypothesis is true and we reject it, also known as a ‘False Positive’. Type II error is committed when the null hypothesis is false and we accept it, also known as ‘False Negative’.

In the context of confusion matrix, we can say Type I error occurs when we classify a value as positive (1) when it is actually negative (0). Type II error occurs when we classify a value as negative (0) when it is actually positive(1).

Imbalanced data is, for example, you have 90% of the data in one class and 10% in other. This leads to problems such as, no predictive power on the other category of data. Here are few techniques to get over it,

Not going too deep in technical, Probability quantifies prediction of outcome, likelihood quantifies trust in model. For instance, someone challenges us to a ‘profitable gambling game’. Then, probabilities will serve us to compute things like the expected profile of your gains and losses. In contrast, likelihood will